IDSVL: Intrusion Detection System Virtual Lab Based on Component in the Internet

2006 ◽  
pp. 264-275 ◽  
Author(s):  
Jianxin Wang ◽  
Ying An ◽  
Yu Sheng ◽  
Shaohua Li
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
The Internet ◽  
Virtual Lab

scholarly journals Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1977 ◽  
Author(s):  
Geethapriya Thamilarasu ◽  
Shiven Chawla
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Cyber Attacks ◽  
The Internet ◽  
Critical Systems ◽  
The Internet Of Things

Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively.

scholarly journals An Anomaly-Based Intrusion Detection System for Internet of Medical Things Networks

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2562
Author(s):  
Georgios Zachos ◽  
Ismael Essop ◽  
Georgios Mantas ◽  
Kyriakos Porfyrakis ◽  
José C. Ribeiro ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Computational Cost ◽  
Healthcare Sector ◽  
The Internet ◽  
Full Potential ◽  
Log Files ◽  
Wide Range ◽  
Internet Of Medical Things

Over the past few years, the healthcare sector is being transformed due to the rise of the Internet of Things (IoT) and the introduction of the Internet of Medical Things (IoMT) technology, whose purpose is the improvement of the patient’s quality of life. Nevertheless, the heterogenous and resource-constrained characteristics of IoMT networks make them vulnerable to a wide range of threats. Thus, novel security mechanisms, such as accurate and efficient anomaly-based intrusion detection systems (AIDSs), considering the inherent limitations of the IoMT networks, need to be developed before IoMT networks reach their full potential in the market. Towards this direction, in this paper, we propose an efficient and effective anomaly-based intrusion detection system (AIDS) for IoMT networks. The proposed AIDS aims to leverage host-based and network-based techniques to reliably collect log files from the IoMT devices and the gateway, as well as traffic from the IoMT edge network, while taking into consideration the computational cost. The proposed AIDS is to rely on machine learning (ML) techniques, considering the computation overhead, in order to detect abnormalities in the collected data and thus identify malicious incidents in the IoMT network. A set of six popular ML algorithms was tested and evaluated for anomaly detection in the proposed AIDS, and the evaluation results showed which of them are the most suitable.

ELNIDS: Ensemble Learning based Network Intrusion Detection System for RPL based Internet of Things

2020 ◽  
Author(s):  
Abhishek Verma ◽  
Virender Ranga
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Ensemble Learning ◽  
Intrusion Detection System ◽  
Detection System ◽  
Smart Devices ◽  
Network Intrusion Detection ◽  
The Internet ◽  
Routing Attacks ◽  
Network Intrusion

Internet of Things is realized by a large number of heterogeneous smart devices which sense, collect and share data with each other over the internet in order to control the physical world. Due to open nature, global connectivity and resource constrained nature of smart devices and wireless networks the Internet of Things is susceptible to various routing attacks. In this paper, we purpose an architecture of Ensemble Learning based Network Intrusion Detection System named ELNIDS for detecting routing attacks against IPv6 Routing Protocol for Low-Power and Lossy Networks. We implement four different ensemble based machine learning classifiers including Boosted Trees, Bagged Trees, Subspace Discriminant and RUSBoosted Trees. To evaluate proposed intrusion detection model we have used RPL-NIDDS17 dataset which contains packet traces of Sinkhole, Blackhole, Sybil, Clone ID, Selective Forwarding, Hello Flooding and Local Repair attacks. Simulation results show the effectiveness of the proposed architecture. We observe that ensemble of Boosted Trees achieve the highest Accuracy of 94.5% while Subspace Discriminant method achieves the lowest Accuracy of 77.8% among classifier validation methods. Similarly, an ensemble of RUSBoosted Trees achieves the highest Area under ROC value of 0.98 while lowest Area under ROC value of 0.87 is achieved by an ensemble of Subspace Discriminant among all classifier validation methods. All the implemented classifiers show acceptable performance results.

scholarly journals Internet of Things: Evolution, Concerns and Security Challenges

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1809
Author(s):  
Parushi Malhotra ◽  
Yashwant Singh ◽  
Pooja Anand ◽  
Deep Kumar Bangotra ◽  
Pradeep Kumar Singh ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Security And Privacy ◽  
Research Assessment ◽  
The Internet ◽  
Digital World ◽  
Network Intrusion ◽  
Depth Analysis

The escalated growth of the Internet of Things (IoT) has started to reform and reshape our lives. The deployment of a large number of objects adhered to the internet has unlocked the vision of the smart world around us, thereby paving a road towards automation and humongous data generation and collection. This automation and continuous explosion of personal and professional information to the digital world provides a potent ground to the adversaries to perform numerous cyber-attacks, thus making security in IoT a sizeable concern. Hence, timely detection and prevention of such threats are pre-requisites to prevent serious consequences. The survey conducted provides a brief insight into the technology with prime attention towards the various attacks and anomalies and their detection based on the intelligent intrusion detection system (IDS). The comprehensive look-over presented in this paper provides an in-depth analysis and assessment of diverse machine learning and deep learning-based network intrusion detection system (NIDS). Additionally, a case study of healthcare in IoT is presented. The study depicts the architecture, security, and privacy issues and application of learning paradigms in this sector. The research assessment is finally concluded by listing the results derived from the literature. Additionally, the paper discusses numerous research challenges to allow further rectifications in the approaches to deal with unusual complications.

scholarly journals An Intrusion Detection System for the Internet of Things Based on Machine Learning: Review and Challenges

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1011
Author(s):  
Ahmed Adnan ◽  
Abdullah Muhammed ◽  
Abdul Azim Abd Ghani ◽  
Azizol Abdullah ◽  
Fahrul Hakim
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Concept Drift ◽  
The Internet ◽  
Ongoing Research ◽  
Active Research ◽  
The Internet Of Things

An intrusion detection system (IDS) is an active research topic and is regarded as one of the important applications of machine learning. An IDS is a classifier that predicts the class of input records associated with certain types of attacks. In this article, we present a review of IDSs from the perspective of machine learning. We present the three main challenges of an IDS, in general, and of an IDS for the Internet of Things (IoT), in particular, namely concept drift, high dimensionality, and computational complexity. Studies on solving each challenge and the direction of ongoing research are addressed. In addition, in this paper, we dedicate a separate section for presenting datasets of an IDS. In particular, three main datasets, namely KDD99, NSL, and Kyoto, are presented. This article concludes that three elements of concept drift, high-dimensional awareness, and computational awareness that are symmetric in their effect and need to be addressed in the neural network (NN)-based model for an IDS in the IoT.

Intrusion Detection System Through Advance Machine Learning for the Internet of Things Networks

2021 ◽  
Vol 23 (2) ◽  
pp. 58-64
Author(s):  
Tanzila Saba ◽  
Tariq Sadad ◽  
Amjad Rehman ◽  
Zahid Mehmood ◽  
Qaisar Javaid
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
The Internet ◽  
The Internet Of Things

scholarly journals An Ensemble of a Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments

2021 ◽  
Vol 13 (18) ◽  
pp. 10057
Author(s):  
Imran ◽  
Faisal Jamil ◽  
Dohyeun Kim
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
The Internet ◽  
Detection Accuracy ◽  
Learning Mechanism ◽  
Detection Systems ◽  
Network Applications ◽  
Network Intrusion

The connectivity of our surrounding objects to the internet plays a tremendous role in our daily lives. Many network applications have been developed in every domain of life, including business, healthcare, smart homes, and smart cities, to name a few. As these network applications provide a wide range of services for large user groups, the network intruders are prone to developing intrusion skills for attack and malicious compliance. Therefore, safeguarding network applications and things connected to the internet has always been a point of interest for researchers. Many studies propose solutions for intrusion detection systems and intrusion prevention systems. Network communities have produced benchmark datasets available for researchers to improve the accuracy of intrusion detection systems. The scientific community has presented data mining and machine learning-based mechanisms to detect intrusion with high classification accuracy. This paper presents an intrusion detection system based on the ensemble of prediction and learning mechanisms to improve anomaly detection accuracy in a network intrusion environment. The learning mechanism is based on automated machine learning, and the prediction model is based on the Kalman filter. Performance analysis of the proposed intrusion detection system is evaluated using publicly available intrusion datasets UNSW-NB15 and CICIDS2017. The proposed model-based intrusion detection accuracy for the UNSW-NB15 dataset is 98.801 percent, and the CICIDS2017 dataset is 97.02 percent. The performance comparison results show that the proposed ensemble model-based intrusion detection significantly improves the intrusion detection accuracy.

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